KU Leuven at HOO-2012: A hybrid approach to detection and correction of determiner and preposition errors in non-native English text
Quan Li, Oleksandr Kolomiyets, Marie‐Francine Moens · Lirias · 2012
In this paper we describe the technical implementation of our system that participated in the Helping Our Own 2012 Shared Task (HOO-2012). The system employs a number of preprocessing steps and machine learning classifiers for correction of determiner and preposition errors in non-native English texts. We use maximum entropy classifiers trained on the provided HOO-2012 development data and a large high-quality English text collection. The system proposes a number of highlyprobable corrections, which are evaluated by a language model and compared with the original text. A number of deterministic rules are used to increase the precision and recall of the system. Our system is ranked among the three best performing HOO-2012 systems with a precision of 31.15%, recall of 22.08 % and F1score of 25.84 % for correction of determiner and preposition errors combined. 1